2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)最新文献_第7页

On the recognition of spontaneous emotions in spoken Chinese 论汉语口语中自发情绪的识别

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304324

Wen Huang, Huixin Zhong, Wenfeng Wang, Chunlin Ji

引用次数: 0

A convolutional attentional neural network for sentiment classification 一种用于情感分类的卷积注意神经网络

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304320

Jiachen Du, Lin Gui, Yulan He, Ruifeng Xu

引用次数: 12

Attention-based LSTM-CNNs for uncertainty identification on Chinese social media texts 基于注意力的lstm - cnn中文社交媒体文本不确定性识别

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304349

Binyang Li, Kaiming Zhou, Wei Gao, Xu Han, Linna Zhou

{"title":"Attention-based LSTM-CNNs for uncertainty identification on Chinese social media texts","authors":"Binyang Li, Kaiming Zhou, Wei Gao, Xu Han, Linna Zhou","doi":"10.1109/SPAC.2017.8304349","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304349","url":null,"abstract":"Uncertainty identification is an important semantic processing task, which is crucial to the quality of information in terms of factuality in many techniques, e.g. topic detection, question answering. Especially in social media, the texts are written informally which are widely used in many applications, so the factuality has become a premier concern. However, existing approaches that still rely on lexical cues suffer greatly from the casual or word-of-mouth peculiarity of social media, in which the cue phrases are often expressed in sub-standard form or even omitted from sentences. To tackle these problems, this paper proposes the attention-based LSTM-CNNs for the uncertainty identification on social media texts, named ALUNI. ALUNI incorporates attention-based LSTM networks to represent the semantics of words, and convolutional neural networks to capture the most important semantics of uncertainty for identification. Experiments are conducted on both Chinese Weibo and news datasets, and 78.19% and 73.95% of F1-measure scores are achieved with 11% and 3% improvement over the baseline, respectively.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126508502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Lattice-based proxy signature scheme with reject sampling method 基于格的拒绝抽样代理签名方案

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304340

Z. L. Jiang, Yudong Liang, Zechao Liu, Xuan Wang

引用次数: 3

Recognition of acoustic effective relaxational lines for gas detection using minimum distance clustering 基于最小距离聚类的气体检测声有效松弛线识别

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304278

Kesheng Zhang, Xiaoxue Guo, Wensheng Hu, Weihua Ou

引用次数: 0

Fast multi-object tracking using convolutional neural networks with tracklets updating 基于轨迹更新的卷积神经网络快速多目标跟踪

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304296

Yuanping Zhang, Yuanyan Tang, Bin Fang, Zhaowei Shang

{"title":"Fast multi-object tracking using convolutional neural networks with tracklets updating","authors":"Yuanping Zhang, Yuanyan Tang, Bin Fang, Zhaowei Shang","doi":"10.1109/SPAC.2017.8304296","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304296","url":null,"abstract":"Many multi-object tracking methods have been developed to solve the computer vision problem which has been attracting significant attentions. In this paper, a novel convolutional neural networks with frame-pair input method for multi-object tracking is presented. It is found that our object tracking methods trained using two successive frames tend to predict the centers of searching windows as the locations of tracked targets. CNN features and color histogram features are extracted as appearance features to measure similarities between objects which used for Tracklets. Kalman Filter and Hungarian algorithm are used to create tracklets association which indicates the location of tracked targets. Specifically, we construct a novel sampling strategy for off-line training. Experiments on the popular challenging datasets show that the proposed tracking system performs on par with recently developed generic multi-object tracking methods, but with much less memory. In addition, our tracking system can run in a speed of over 80 (30) fps with a GPU (CPU), much faster than most deep neural networks based trackers. We found that simply improving detection performance can lead to much better multiple object tracking results.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"639 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123076709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

CNN-based color image encryption algorithm using DNA sequence operations 基于cnn的彩色图像加密算法，使用DNA序列运算

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304370

Jingshuai Wang, Fei Long, Weihua Ou

{"title":"CNN-based color image encryption algorithm using DNA sequence operations","authors":"Jingshuai Wang, Fei Long, Weihua Ou","doi":"10.1109/SPAC.2017.8304370","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304370","url":null,"abstract":"The encryption algorithm of color images based on chaos theory has attracted lots of attentions in recent years. Nevertheless, due to the defects of the low dimensional chaotic system in single structure and small key space size, the security of cryptosystem is not sufficient enough. In this paper, we proposed a novel encryption algorithm for color images based on Deoxyribonucleic acid (DNA) sequence operations and cellular neural network (CNN) to dispose of these defects. The proposed cryptosystem of this paper takes on the features of large key space and complex structure. Firstly, the plain color image is split into three matrices (R, G, B) which are transformed into DNA matrices by the DNA encoding rules, respectively. Secondly, the elements' positions of the three DNA sequence matrices are scrambled via the chaotic sequences generated by CNN. Thirdly, the three DNA matrices are summed according to the certain rules and complemented by the complementary rules, and then the cipher-image is obtained by the DNA decoding rules via the DNA matrices. Simulation results and security analysis show that the encryption effect of this paper is not only better than traditional encryption algorithms but also has excellent ability to hold back familiar attacks.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"39 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113937972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

Probabilistic topic model based approach for detecting bursty events from social media data 基于概率主题模型的社交媒体突发事件检测方法

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304365

Chunshan Li, Dianhui Chu

{"title":"Probabilistic topic model based approach for detecting bursty events from social media data","authors":"Chunshan Li, Dianhui Chu","doi":"10.1109/SPAC.2017.8304365","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304365","url":null,"abstract":"To detect bursty events from the huge amount of real-time data generated from various social networks has attracted more and more research efforts. Most of existing algorithms detect the bursty events either by discovering the co-occurrent bursty words or the emerging topics, ignoring the association between bursty and topics. Meanwhile, these algorithms are not able to cope with short text data like Weibo and Twitter. This paper proposes two novel probabilistic generative models (TBE/TBEP). TBE model can detect bursty events on long articles which can simultaneously consider the co-occurrent relationships among bursty words as well as the co-occurrent relationships among occurrent words and the underlying topics which generate the bursty events. TBEP model captures the assumption: one post are always have the one topic, which can handle the bursty events on Weibo and Twitter. The Gibbs sampling technique is adopted to estimate the model parameters. Extensive experiments are performed on three real data sets and the promising results, compared with the state-of-the-art Hot-Bursty-Event detection algorithms, have demonstrated that the proposed approach can: (1) achieve better model performance with respect to the evaluation criteria; (2) achieve more accurate bursty evnets on long/short text data.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115831479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Application of the neural network in diagnosis of breast cancer based on levenberg-marquardt algorithm 神经网络在levenberg-marquardt算法乳腺癌诊断中的应用

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304288

Zeng Min, Liang Xiao, Lin Cao, Hangcheng

引用次数: 5

Research on path planning algorithm and its application based on terrain slope for slipping prediction in complex terrain environment 基于地形坡度的路径规划算法及其在复杂地形环境滑动预测中的应用研究

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304280

Lanfeng Zhou, Lina Yang, Hanwei Tang

引用次数: 4